Schneider Electric Cooling Architect: Liquid Cooling Orchestration Does Not Track Instantaneous GPU Load but Schedules Setpoints Across the Entire Heat Dissipation Chain
Amir Ibraheem, Cooling Solutions Architect at Schneider Electric, explains the control architecture for AI data center liquid cooling orchestration: local CDU control loops maintain supply temperature and differential pressure, while the orchestration layer continuously resets setpoints for heat rejection facilities, chilled water plants, CDUs, and secondary fluid networks at a higher level. Using predictive models to adjust capacity before heat reaches the cooling loop, it maintains thermal headroom at the cold plate with minimum energy and water consumption.
AI workloads can fluctuate on a second-by-second basis, but the thermal, hydraulic, and mechanical systems supporting liquid cooling respond much more slowly. Amir Ibraheem, Cooling Solutions Architect at Schneider Electric, points out that the goal of liquid cooling orchestration is not to track every individual change in GPU load, but to continuously optimize the end-to-end cooling ecosystem over longer time scales, ensuring that cooling capacity is available when needed while minimizing energy, water, and infrastructure overhead.
This objective is achieved through a two-layer control architecture. The local CDU control loop is responsible for maintaining supply temperature and differential pressure within expected operational ranges—a fast, component-level response. The orchestration layer operates at a higher supervisory level, continuously resetting setpoints for heat rejection facilities, chilled water plants, CDUs, and secondary fluid networks to maintain adequate thermal headroom at the cold plate with minimal energy and water usage.
The connection between the two layers relies on thermal buffering. Fluid volume, metal mass, and allowable temperature rise provide temporary buffers, allowing slower equipment time to respond. Orchestration software learns from historical and real-time data, using predictive models to identify sustained thermal demand trends and proactively adjusting setpoints before heat reaches the cooling loop.